Question

我使用tensorflow进行文本匹配，在我调用tf.nn.embedding_lookup(word_embedding_matrix, combine_result)之前，我必须结合2个句子中的一些单词（从句子S1中获取m个单词，并从句子S2中获取m个单词，然后结合他们在一起作为＆＃34; combine_result＆＃34;），但当代码出现在tf.nn.embedding_lookup(word_embedding_matrix, combine_result)时，它给了我错误：

ValueError：Tensor（＆＃34; Reshape_7：0＆＃34;，shape =（1,6），dtype = int32）必须是与Tensor相同的图表（＆＃34; word_embedding_matrix：0＆＃34;，shape =（26320， 50），dtype = float32_ref）。

代码如下：

import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers

NUM_CLASS = 2
SEQUENCE_LENGTH = 47


    # Placeholders for input, output and dropout
    input_x = tf.placeholder(tf.int32, [None, 2, SEQUENCE_LENGTH], name="input_x")
    input_y = tf.placeholder(tf.float32, [None, NUM_CLASS], name="input_y")
    dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")

    def n_grams(text, window_size):
                text_left_window = []
                # text_left_window = tf.convert_to_tensor(text_left_window, dtype=tf.int32)
                for z in range(SEQUENCE_LENGTH-2):
                    text_left = tf.slice(text, [z], [window_size])
                    text_left_window = tf.concat(0, [text_left_window, text_left])
                text_left_window = tf.reshape(text_left_window, [-1, window_size])
                return text_left_window


            def inference(vocab_size, embedding_size, batch_size, slide_window_size, conv_window_size):
                # # Embedding layer
                word_embedding_matrix = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
                                                    name="word_embedding_matrix")
                # convo_unit = tf.Variable(tf.random_uniform([slide_window_size*2, ], -1.0, 1.0), name="convo_unit")

                text_comp_result = []
                for x in range(batch_size):
                    # input_x_slice_reshape = [[1 1 1...]
                    #                           [2 2 2...]]
                    input_x_slice = tf.slice(input_x, [x, 0, 0], [1, 2, SEQUENCE_LENGTH])
                    input_x_slice_reshape = tf.reshape(input_x_slice, [2, SEQUENCE_LENGTH])

                    # text_left_flat: [294, 6, 2, 6, 2, 57, 2, 57, 147, 57, 147, 5, 147, 5, 2,...], length = SEQUENCE_LENGTH
                    # text_right_flat: [17, 2, 2325, 2, 2325, 5366, 2325, 5366, 81, 5366, 81, 1238,...]
                    text_left = tf.slice(input_x_slice_reshape, [0, 0], [1, SEQUENCE_LENGTH])
                    text_left_flat = tf.reshape(text_left, [-1])
                    text_right = tf.slice(input_x_slice_reshape, [1, 0], [1, SEQUENCE_LENGTH])
                    text_right_flat = tf.reshape(text_right, [-1])

                    # extract both text.
                    # text_left_window: [[294, 6, 2], [6, 2, 57], [2, 57, 147], [57, 147, 5], [147, 5, 2],...]
                    # text_right_window: [[17, 2, 2325], [2, 2325, 5366], [2325, 5366, 81], [5366, 81, 1238],...]
                    text_left_window = n_grams(text_left_flat, slide_window_size)
                    text_right_window = n_grams(text_right_flat, slide_window_size)
                    text_left_window_sha = text_left_window.get_shape()
                    print 'text_left_window_sha:', text_left_window_sha

                    # composite the slice
                    text_comp_list = []
                    # text_comp_list = tf.convert_to_tensor(text_comp_list, dtype=tf.float32)
                    for l in range(SEQUENCE_LENGTH-slide_window_size+1):
                        text_left_slice = tf.slice(text_left_window, [l, 0], [1, slide_window_size])
                        text_left_slice_flat = tf.reshape(text_left_slice, [-1])
                        for r in range(SEQUENCE_LENGTH-slide_window_size+1):
                            text_right_slice = tf.slice(text_right_window, [r, 0], [1, slide_window_size])
                            text_right_slice_flat = tf.reshape(text_right_slice, [-1])

                            # convo_unit = [294, 6, 2, 17, 2, 2325]
                            convo_unit = tf.concat(0, [text_left_slice_flat, text_right_slice_flat])
                            convo_unit_reshape = tf.reshape(convo_unit, [-1, slide_window_size*2])
                            # convo_unit_shape_val = convo_unit_reshape.get_shape()
                            # print 'convo_unit_shape_val:', convo_unit_shape_val

                            embedded_chars = tf.nn.embedding_lookup(word_embedding_matrix, convo_unit_reshape)
                            embedded_chars_expanded = tf.expand_dims(embedded_chars, -1)
        ...

可以请别人帮帮我吗？非常感谢你！

Answer 1

雅罗斯拉夫在上面的评论中回答 - 转到答案：

创建新的默认图表时会发生此错误。尝试在计算之前做tf.reset_default_graph()并且不再创建图表（即调用tf.Graph）

ValueError：Tensor A必须与Tensor B在同一图表中

1 个答案: